SoroushVahidi/scidocs huggingface dataset:
Fast and Accurate Detection and Classification of Plant Diseases
(2011)

We propose and experimentally evaluate a software solution for automatic detection and classification of plant leaf diseases. The proposed solution is an improvement to the solution proposed in [1] as it provides faster and more accurate solution. The developed processing scheme consists of four main phases as in [1]. The following two steps are added successively after the segmentation phase. In the first step we identify the mostlygreen colored pixels. Next, these pixels are masked based on specific threshold values that are computed using Otsu's method, then those mostly green pixels are masked. The other additional step is that the pixels with zeros red, green and blue values and the pixels on the boundaries of the infected cluster (object) were completely removed. The experimental results demonstrate that the proposed technique is a robust technique for the detection of plant leaves diseases. The developed algorithm‟s efficiency can successfully detect and classify the examined diseases with a precision between 83% and 94%, and can achieve 20% speedup over the approach proposed in [1].
Learning temporal features using LSTM-CNN architecture for face anti-spoofing
(2015)

Temporal features is important for face anti-spoofing. Unfortunately existing methods have limitations to explore such temporal features. In this work, we propose a deep neural network architecture combining Long Short-Term Memory (LSTM) units with Convolutional Neural Networks (CNN). Our architecture works well for face anti-spoofing by utilizing the LSTM units' ability of finding long relation from its input sequences as well as extracting local and dense features through convolution operations. Our best model shows significant performance improvement over general CNN architecture (5.93% vs. 7.34%), and hand-crafted features (5.93% vs. 10.00%) on CASIA dataset.
Robust kernel density estimation
(2008)

In this paper, we propose a method for robust kernel density estimation. We interpret a KDE with Gaussian kernel as the inner product between a mapped test point and the centroid of mapped training points in kernel feature space. Our robust KDE replaces the centroid with a robust estimate based on M-estimation (P. Huber, 1981), The iteratively re-weighted least squares (IRWLS) algorithm for M-estimation depends only on inner products, and can therefore be implemented using the kernel trick. We prove the IRWLS method monotonically decreases its objective value at every iteration for a broad class of robust loss functions. Our proposed method is applied to synthetic data and network traffic volumes, and the results compare favorably to the standard KDE.
Risk and maintenance factors for eating pathology: a meta-analytic review.
(2002)

This meta-analytic review of prospective and experimental studies reveals that several accepted risk factors for eating pathology have not received empirical support (e.g., sexual abuse) or have received contradictory support (e.g.. dieting). There was consistent support for less-accepted risk factors(e.g., thin-ideal internalization) as well as emerging evidence for variables that potentiate and mitigate the effects of risk factors(e.g., social support) and factors that predict eating pathology maintenance(e.g., negative affect). In addition, certain multivariate etiologic and maintenance models received preliminary support. However, the predictive power of individual risk and maintenance factors was limited, suggesting it will be important to search for additional risk and maintenance factors, develop more comprehensive multivariate models, and address methodological limitations that attenuate effects.
Principal direction analysis-based real-time 3D human pose reconstruction from a single depth image
(2013)

Human pose estimation in real-time is a challenging problem in computer vision. In this paper, we present a novel approach to recover a 3D human pose in real-time from a single depth human silhouette using Principal Direction Analysis (PDA) on each recognized body part. In our work, the human body parts are first recognized from a depth human body silhouette via the trained Random Forests (RFs). On each recognized body part which is presented as a set of 3D points cloud, PDA is applied to estimate the principal direction of the body part. Finally, a 3D human pose gets recovered by mapping the principal directional vector to each body part of a 3D human body model which is created with a set of super-quadrics linked by the kinematic chains. In our experiments, we have performed quantitative and qualitative evaluations of the proposed 3D human pose reconstruction methodology. Our evaluation results show that the proposed approach performs reliably on a sequence of unconstrained poses and achieves an average reconstruction error of 7.46 degree in a few key joint angles. Our 3D pose recovery methodology should be applicable to many areas such as human computer interactions and human activity recognition.
An experimental study on the applicability of evolutionary algorithms to craniofacial superimposition in forensic identification
(2009)

0020-0255/$ see front matter 2009 Elsevier Inc doi:10.1016/j.ins.2008.12.029 * Corresponding author. Tel.: +34 985 456545; fa E-mail address: oscar.ibanez@softcomputing.es ( Photographic supra-projection is a forensic process that aims to identify a missing person from a photograph and a skull found. One of the crucial tasks throughout all this process is the craniofacial superimposition which tries to find a good fit between a 3D model of the skull and the 2D photo of the face. This photographic supra-projection stage is usually carried out manually by forensic anthropologists. It is thus very time consuming and presents several difficulties. In this paper, we aim to demonstrate that real-coded evolutionary algorithms are suitable approaches to tackle craniofacial superimposition. To do so, we first formulate this complex task in forensic identification as a numerical optimization problem. Then, we adapt three different evolutionary algorithms to solve it: two variants of a real-coded genetic algorithm and the state of the art evolution strategy CMA-ES. We also consider an existing binary-coded genetic algorithm as a baseline. Results on several superimposition problems of real-world identification cases solved by the Physical Anthropology lab at the University of Granada (Spain) are considered to test our proposals. 2009 Elsevier Inc. All rights reserved.
Noise adaptive soft-switching median filter
(2001)

Existing state-of-the-art switching-based median filters are commonly found to be nonadaptive to noise density variations and prone to misclassifying pixel characteristics at high noise density interference. This reveals the critical need of having a sophisticated switching scheme and an adaptive weighted median filter. We propose a novel switching-based median filter with incorporation of fuzzy-set concept, called the noise adaptive soft-switching median (NASM) filter, to achieve much improved filtering performance in terms of effectiveness in removing impulse noise while preserving signal details and robustness in combating noise density variations. The proposed NASM filter consists of two stages. A soft-switching noise-detection scheme is developed to classify each pixel to be uncorrupted pixel, isolated impulse noise, nonisolated impulse noise or image object's edge pixel. "No filtering" (or identity filter), standard median (SM) filter or our developed fuzzy weighted median (FWM) filter will then be employed according to the respective characteristic type identified. Experimental results show that our NASM filter impressively outperforms other techniques by achieving fairly close performance to that of ideal-switching median filter across a wide range of noise densities, ranging from 10% to 70%
Tri-state median filter for image denoising
(1999)

In this work, a novel nonlinear filter, called tri-state median (TSM) filter, is proposed for preserving image details while effectively suppressing impulse noise. We incorporate the standard median (SM) filter and the center weighted median (CWM) filter into a noise detection framework to determine whether a pixel is corrupted, before applying filtering unconditionally. Extensive simulation results demonstrate that the proposed filter consistently outperforms other median filters by balancing the tradeoff between noise reduction and detail preservation.
Nonlinear Modeling of the Self-Oscillating Fluxgate Current Sensor
(2007)

An accurate mathematical model has been developed in order to describe electrical and geometrical factors affecting operation and DC transfer characteristics of the self-oscillating fluxgate current sensor. The mathematical description of the sensor circuit is based on a continuous function that approximates magnetization curve of the used transformer core. The circuit differential equation based on the adopted transformer model is analytically solved offering a fundamental basis for understanding factors that affect the operation of the sensor. All principle parameters affecting sensor operation are clearly demonstrated by using the proposed model, and good agreements with laboratory measurements has been obtained.
Integrated bioethanol and protein production from brown seaweed Laminaria digitata.
(2015)

A wild-growing glucose-rich (i.e. 56.7% glucose content) brown seaweed species Laminaria digitata, collected from the North Coast of Denmark in August 2012, was used as the feedstock for an integrated bioethanol and protein production. Glutamic acid and aspartic acid are the two most abundant amino acids in the algal protein, both with proportional content of 10% in crude protein. Only minor pretreatment of milling was used on the biomass to facilitate the subsequent enzymatic hydrolysis and fermentation. The Separate Hydrolysis and Fermentation (SHF) resulted in obviously higher ethanol yield than the Simultaneous Saccharification and Fermentation (SSF). High conversion rate at maximum of 84.1% glucose recovery by enzymatic hydrolysis and overall ethanol yield at maximum of 77.7% theoretical were achieved. Protein content in the solid residues after fermentation was enriched by 2.7 fold, with similar distributions of amino acids, due to the hydrolysis of polymers in the seaweed cell wall matrix.
Dynamic Thermal Management for High-Performance Microprocessors
(2001)

With the increasing clock rate and transistor count of today’s microprocessors, power dissipation is becoming a critical component of system design complexity. Thermal and power-delivery issues are becoming especially critical for high-performance computing systems. In this work, we investigate dynamic thermal management as a technique to control CPUpower dissipation. With the increasing usage of clock gating techniques, the average power dissipation typically seen by common applications is becoming much less than the chip’s rated maximum power dissipation. However; system designers still must design thermal heat sinks to withstand the worst-case scenario. We define and investigate the major components of any dynamic thermal management scheme. Specijcally we explore the tradeoffs between several mechanisms for responding to periods of thermal trauma and we consider the effects of hardware and sofnyare implementations. With appropriate dynamic thermal management, the CPU can be designed for a much lower maximum power rating, with minimal performance impact for typical applications.
Efficient elastic burst detection in data streams
(2003)

Burst detection is the activity of finding abnormal aggregates in data streams. Such aggregates are based on sliding windows over data streams. In some applications, we want to monitor many sliding window sizes simultaneously and to report those windows with aggregates significantly different from other periods. We will present a general data structure for detecting interesting aggregates over such elastic windows in near linear time. We present applications of the algorithm for detecting Gamma Ray Bursts in large-scale astrophysical data. Detection of periods with high volumes of trading activities and high stock price volatility is also demonstrated using real time Trade and Quote (TAQ) data from the New York Stock Exchange (NYSE). Our algorithm beats the direct computation approach by several orders of magnitude.
Learning to Assign Orientations to Feature Points
(2016)

We show how to train a Convolutional Neural Network to assign a canonical orientation to feature points given an image patch centered on the feature point. Our method improves feature point matching upon the state-of-the art and can be used in conjunction with any existing rotation sensitive descriptors. To avoid the tedious and almost impossible task of finding a target orientation to learn, we propose to use Siamese networks which implicitly find the optimal orientations during training. We also propose a new type of activation function for Neural Networks that generalizes the popular ReLU, maxout, and PReLU activation functions. This novel activation performs better for our task. We validate the effectiveness of our method extensively with four existing datasets, including two non-planar datasets, as well as our own dataset. We show that we outperform the state-of-the-art without the need of retraining for each dataset.
Compact CPW-Fed microstrip octagonal patch antenna with H slot for WLAN and WIMAX applications
(2017)

In this paper, a Coplanar Wave Guide (CPW)-Fed microstrip octagonal patch antenna for WLAN and WIMAX Applications is proposed. The studied structure is suitable for 2.3/2.5/3.3/3.5/5/5.5GHz WiMAX and for 3.6/2.4–2.5/4.9–5.9GHz WLAN applications. The octagonal shape is obtained by making a cut in the four angles of the rectangular microstrip patch antenna; in addition the using of CPW-Fed allows obtaining UWB characteristics. The miniaturization in the antenna size for lower band is achieved by introducing an H slot in the radiating element. The proposed antenna is designed on a single and a small substrate board of dimensions 46×40×1.6 mm3. Moreover the miniaturized antenna has a good impedance matching and a reasonable gain. All the simulations were performed in CADFEKO, a Method of Moment (MoM) based solver.
Optimized Product Quantization for Approximate Nearest Neighbor Search
(2013)

Product quantization is an effective vector quantization approach to compactly encode high-dimensional vectors for fast approximate nearest neighbor (ANN) search. The essence of product quantization is to decompose the original high-dimensional space into the Cartesian product of a finite number of low-dimensional subspaces that are then quantized separately. Optimal space decomposition is important for the performance of ANN search, but still remains unaddressed. In this paper, we optimize product quantization by minimizing quantization distortions w.r.t. the space decomposition and the quantization codebooks. We present two novel methods for optimization: a non-parametric method that alternatively solves two smaller sub-problems, and a parametric method that is guaranteed to achieve the optimal solution if the input data follows some Gaussian distribution. We show by experiments that our optimized approach substantially improves the accuracy of product quantization for ANN search.
A GPU-Based Rasterization Algorithm for Boolean Operations on Polygons
(2018)

This paper presents a new GPU-based rasterization algorithm for Boolean operations that handles arbitary closed polygons. We construct an efficient data structure for interoperation of CPU and GPU and propose a fast GPU-based contour extraction method to ensure the performance of our algorithm. We then design a novel traversing strategy to achieve an error-free calculation of intersection point for correct Boolean operations. We finally give a detail evaluation and the results show that our algorithm has a higher performance than exsiting algorithms on processing polygons with large amount of vertices. key words: GPU, CPU, rasterization, Boolean operation, error-free
Experimental Analysis of Mode Switching Techniques in Touch-based User Interfaces
(2017)

This paper presents the results of a 36 participant empirical comparison of touch mode-switching. Six techniques are evaluated, spanning current and future techniques: long press, non-dominant hand, two-fingers, hard press, knuckle, and thumb-on-finger. Two poses are controlled for, seated with the tablet on a desk and standing with the tablet held on the forearm. Findings indicate pose has no effect on mode switching time and little effect on error rate; using two-fingers is fastest while long press is much slower; non-preferred hand and thumb-on-finger also rate highly in subjective scores. The experiment protocol is based on Li et al.'s pen mode-switching study, enabling a comparison of touch and pen mode switching. Among the common techniques, the non-dominant hand is faster than pressure with touch, whereas no significant difference had been found for pen. Our work addresses the lack of empirical evidence comparing touch mode-switching techniques and provides guidance to practitioners when choosing techniques and to researchers when designing new mode-switching methods.
Backtracking search algorithm in CVRP models for efficient solid waste collection and route optimization.
(2017)

Waste collection is an important part of waste management that involves different issues, including environmental, economic, and social, among others. Waste collection optimization can reduce the waste collection budget and environmental emissions by reducing the collection route distance. This paper presents a modified Backtracking Search Algorithm (BSA) in capacitated vehicle routing problem (CVRP) models with the smart bin concept to find the best optimized waste collection route solutions. The objective function minimizes the sum of the waste collection route distances. The study introduces the concept of the threshold waste level (TWL) of waste bins to reduce the number of bins to be emptied by finding an optimal range, thus minimizing the distance. A scheduling model is also introduced to compare the feasibility of the proposed model with that of the conventional collection system in terms of travel distance, collected waste, fuel consumption, fuel cost, efficiency and CO2 emission. The optimal TWL was found to be between 70% and 75% of the fill level of waste collection nodes and had the maximum tightness value for different problem cases. The obtained results for four days show a 36.80% distance reduction for 91.40% of the total waste collection, which eventually increases the average waste collection efficiency by 36.78% and reduces the fuel consumption, fuel cost and CO2 emission by 50%, 47.77% and 44.68%, respectively. Thus, the proposed optimization model can be considered a viable tool for optimizing waste collection routes to reduce economic costs and environmental impacts.
AtlanticWave-SDX : An International SDX to Support Science Data Applications
(2015)

New scientific instruments that are being designed and deployed in the coming years will dramatically increase the need for large, real-time data transfers among scientists throughout the world. One such instrument is the Large Synoptic Survey Telescope being built in Chile that will produce 6.4 GB images every 17 seconds. This paper describes an ongoing effort to meet the demands of these large data scientific instruments through the development of an international software defined exchange point (SDX) that will meet the provisioning needs for the scientific users. The specific planned and ongoing work in SDX architecture is described with specific consideration for policy specification and security. Keywords—Software Defined Exchange; Science Data
Pustulobullous variant of SDRIFE (symmetrical drug-related intertriginous and flexural exanthema).
(2017)

We report on a 64-year-old patient from Sri Lanka who presented to the emergency room with a 24-hour history of pruritic, erythematous patches – with occasional pustules and blisters – in the groins, axillae, and antecubital fossae. The patient reported no pain or general symptoms. Six days earlier, he had started taking cefuroxime (500 mg BID) for the first time. The antibiotic had been prescribed following temporary ureteral stent placement due to nephrolithiasis. He denied any other medical or dermatological conditions, and no other drugs had been taken. At initial presentation, he exhibited sharply demarcated, symmetrically disseminated, dark-red patches in the groins, axillae, and antecubital fossae. Some lesions showed pustules and blisters (Figure 1a–c). The physical exam was otherwise unremarkable; in particular, there was no fever. Histology revealed a superficial perivascular, spongiform dermatitis with lymphocytes, eosinophils, and neutrophils. Focally, necrotic keratinocytes and neutrophils were noted in the epidermis. Overall, the findings were consistent with a drug reaction (Figure 2). Apart from slightly elevated CRP levels (5.7 mg/dL, normal range: < 0.5) and mild thrombocytopenia (111,000, normal range: 166,000–308,000), laboratory tests were within normal limits. The detection of Escherichia coli in a swab taken from a pustule in the inguinal region was thought to represent contamination; the pathological lab results were considered to be consistent with an inflammatory response to nephrolithiasis. Taking the history, clinical symptoms, and histological findings into account, the patient was diagnosed with a pustulobullous variant of SDRIFE (symmetrical drug-related intertriginous and flexural exanthema). Since 2004, the acronym SDRIFE has been used for the condition previously known as baboon syndrome. First reported in 1984, the term baboon syndrome referred to the acute development of erythematous lesions in the gluteal area following contact with mercury from broken fever thermometers [1, 2]. In particular, the new term SDRIFE takes into account that systemic drugs, too, may induce flexural skin lesions without prior sensitization [3–6]. In the present case, the development of skin lesions six days after the intake of cefuroxime suggests first exposure to the drug. The diagnosis of SDRIFE includes five clinical criteria that are summarized in Table 1. Atypical disease courses with pustules, papules, and blisters have also been described [4]. The onset of SDRIFE is independent of patient age, and occurs a few hours or even up to eight days after the administration/application of the triggering factor. Apart from systemic antibiotics – in particular β-lactam antibiotics – corticosteroids, psychopharmaceuticals, biologics, and many other drug classes are
High Efficient Interleaved Multi-channel dc/dc Converter Dedicated to Mobile Applications
(2006)

In the transportation field, the empty weight of the vehicle has to be as light as possible in order to have the highest energetic efficiency. For onboard storage electrical systems, DC/DC converters are usually used to control the power flows in the vehicle. This paper focuses on two different DC/DC converter applications: high current/low voltage on the one hand and low current/high voltage on the other hand. For all power mobile applications, a standard dc/dc converter is not realizable due to mass and volume constraints. As the inductor is usually the biggest and heaviest component in the system, it is important to develop a new structure allowing the reduction of its size and weight. The chosen technology is based on interleaved channels operating in discontinuous mode, also using reverse conduction of power MOSFET transistors. For both of the cases described above, a prototype has been built composed of 8 and 4 channels respectively. The first converter is used for power assistance (250 A/18 V) using the MOS reverse conduction property. In this particular case, efficiency is up to 96.3 %. The second is developed for a mobile solar application (3.8 A/270 V). Efficiencies are up to 98.4 %